[InetBib] Submission Deadline Extension: 2nd Workshop on Scholarly Document Processing at NAACL 2021

["Mayr-Schlegel, Philipp via InetBib" <inetbib@xxxxxxxxxx>]

Dear colleagues,
 
You are invited to participate in the 2nd Workshop on Scholarly Document 
Processing (SDP 2021) on June 10 at NAACL 2021 (June 6-11). The SDP 2021 
workshop will consist of a Research track and 3 Shared Tasks.  The call for 
research papers is described below, and more details can be found on our 
website (https://sdproc.org/).

*** The Title & Abstract submission deadline is March 15, 2021. ***
*** The Paper submission deadline is extended to March 19, 2021.  (Changes to 
previously-submitted title & abstract are allowed). ***
*** These deadlines apply to the main Research Track and the LongSumm & SciVER 
shared tasks. ***
*** The 3C shared task ends on April 30, 2021, and has a submission deadline of 
May 10, 2021. ***
 
Papers must follow the NAACL Format 
(https://2021.naacl.org/calls/style-and-formatting/) and conform to the NAACL 
Submission Guidelines (https://2021.naacl.org/calls/papers/first/). Paper 
submission has to be done through the Softconf system: 
https://www.softconf.com/naacl2021/sdp2021/ 
 
Website: http://www.sdproc.org/
Twitter: https://twitter.com/sdproc 
Mailing list:  https://groups.google.com/g/sdproc-updates
Main Research Track:  https://sdproc.org/2021/cfp.html
LongSumm 2021 shared task:  https://sdproc.org/2021/sharedtasks.html#longsumm
SCIVER shared task:  https://sdproc.org/2021/sharedtasks.html#sciver
3C shared task:  https://sdproc.org/2021/sharedtasks.html#3c

 
== Call for papers ==
 
** Introduction **
 
Although scientific literature plays a major part in research and 
policy-making, these texts represent an underserved area of NLP. NLP can play a 
role in addressing research information overload, identifying disinformation 
and its effect on people and society, and enhancing the reproducibility of 
science. The unique challenges of processing scholarly documents necessitate 
the development of specific methods and resources optimized for this domain. 
The Scholarly Document Processing (SDP) workshop provides a venue for 
discussing these challenges, bringing together stakeholders from different 
communities including computational linguistics, text mining, information 
retrieval, digital libraries, scientometrics, and others to develop and present 
methods and resources in support of these goals. 
 
This workshop builds on the success of prior workshops: the 1st SDP workshop 
held at EMNLP 2020 and the 1st SciNLP workshop held at AKBC 2020. In addition 
to having broad appeal within the NLP community, we hope the SDP workshop will 
attract researchers from other relevant fields including meta-science, 
scientometrics, data mining, information retrieval, and digital libraries, 
bringing together these disparate communities within ACL.
 

** Topics of Interest **
 
We invite submissions from all communities demonstrating usage of and 
challenges associated with natural language processing, information retrieval, 
and data mining of scholarly and scientific documents. Relevant tasks include: 

* Representation learning
* Information extraction
* Summarization
* Generation
* Question answering
* Discourse modeling and argumentation mining
* Network analysis
* Bibliometrics, scientometrics, and altmetrics
* Reproducibility
* Peer review
* Search and indexing
* Datasets and resources
* Document parsing
* Text mining
* Research infrastructure, and others.
 
We specifically invite research on important and/or underserved areas, such as:

* Identifying/mitigating scientific disinformation and its effects on public 
policy and behavior
* Reducing  information  overload  through  summarization   and   aggregation   
of   information within and across documents
* Improving  access  to  scientific  papers  through multilingual scholarly 
document processing
 
 
** Submission Information **
 
Authors are invited to submit full and short papers with unpublished, original 
work. Submissions will be subject to a double-blind peer review process. 
Accepted papers will be presented by the authors at the workshop either as a 
talk or a poster. All accepted papers will be published in the workshop 
proceedings.
  
The submissions should be in PDF format and anonymized for review. All 
submissions must be written in English and follow the NAACL 2021 formatting 
requirements: https://2021.naacl.org/calls/style-and-formatting/ 
 
We follow the same policies as NAACL 2021 regarding preprints and 
double-submissions. The anonymity period for SDP 2021 is from February 15, 2021 
to April 15, 2021.  
 
Long paper submissions: up to 8 pages of content, plus unlimited references.
Short paper submissions: up to 4 pages of content, plus unlimited references.
 
Final versions of accepted papers will be allowed 1 additional page of content 
so that reviewer comments can be taken into account.
 
More details about submissions are available on our website: 
http://www.sdproc.org/. To receive updates, please join our mailing list: 
https://groups.google.com/g/sdproc-updates or follow us on Twitter: 
https://twitter.com/sdproc 
 
 
** Important Dates ** 
 
* 1st Call for Workshop Papers – December 6, 2020
* 2nd Call for Workshop Papers – March 1, 2021
* Title & abstract submissions due – March 15, 2021
* All paper submissions due – March 19, 2021
* Notification of acceptance – April 15, 2021
* Camera-ready papers due – April 26, 2021
* Workshop – June 10, 2021
  
** 3C shared task - New Dates **

* Competition end date – April 30, 2021
* Paper and code submission deadline – May 10, 2021
* Shared task acceptance notification – May 25, 2021
* Camera-ready papers due – June 03, 2021
* Workshop – June 10, 2021
 
 
** SDP 2021 Keynote Speakers **

We’re excited to have 3 keynote speakers at SDP 2021:
 
* Hannaneh Hajishirzi, University of Washington and AI2
* Yoav Goldberg, Bar Ilan University and AI2 Israel
* Isabelle Augenstein, University of Copenhagen
 
 
** Organizing Committee **

Iz Beltagy, Allen Institute for AI, Seattle, USA
Arman Cohan, Allen Institute for AI, Seattle, USA
Guy Feigenblat, IBM Research AI, Haifa Research Lab, Israel
Dayne Freitag, SRI International, San Diego, USA
Tirthankar Ghosal, Indian Institute of Technology Patna, India
Keith Hall, Google Research, New York, USA
Drahomira Herrmannova, Oak Ridge National Laboratory, USA
Petr Knoth, Open University, UK
Kyle Lo, Allen Institute for AI, Seattle, USA
Philipp Mayr, GESIS -- Leibniz Institute for the Social Sciences, Germany
Robert M. Patton, Oak Ridge National Laboratory, USA
Michal Shmueli-Scheuer, IBM Research AI, Haifa Research Lab, Israel
Anita de Waard, Elsevier, USA
Kuansan Wang, Microsoft Research, Redmond, USA
Lucy Lu Wang, Allen Institute for AI, Seattle, USA



Best regards,

-- 
Drahomira Herrmannova (Dasha), Ph.D. 
Research Scientist, Learning Systems
Oak Ridge National Laboratory
https://orcid.org/0000-0002-2730-1546
http://dasha.tech

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